




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__            = logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase__            = []
for i in range(6):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
		)
		rename_keys.append(
		    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
		)
		rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
		rename_keys.append(
		    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
		)
		rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
		)
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
		)
		rename_keys.append(
		    (
		        F"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
		        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
		    )
		)
		rename_keys.append(
		    (
		        F"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
		        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
		    )
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
		)
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

		# q, k, v projections in self/cross-attention in decoder for conditional DETR
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
		)
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
		)
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
		)
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
		)
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
		)

		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
		)
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
		)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		List[Any]						,						SCREAMING_SNAKE_CASE						:		Dict						,						SCREAMING_SNAKE_CASE						:		Optional[int] ):






	"""simple docstring"""
	snake_case__						:     Any		=state_dict.pop(UpperCamelCase_ )
	snake_case__						:     int		=val







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Optional[int] ):






	"""simple docstring"""
	snake_case__						:     int		=OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			snake_case__						:     Dict		=key.replace('''backbone.0.body'''						,						'''backbone.conv_encoder.model''' )
			snake_case__						:     List[Any]		=value
		else:
			snake_case__						:     List[str]		=value

	return new_state_dict







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Dict						,						SCREAMING_SNAKE_CASE						:		List[str]=False ):






	"""simple docstring"""
	snake_case__						:     Union[str, Any]		=''''''
	if is_panoptic:
		snake_case__						:     List[Any]		='''conditional_detr.'''

	# first: transformer encoder
	for i in range(6 ):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		snake_case__						:     List[Any]		=state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
		snake_case__						:     Tuple		=state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
		# next, add query, keys and values (in that order) to the state dict
		snake_case__						:     Any		=in_proj_weight[:2_56, :]
		snake_case__						:     Dict		=in_proj_bias[:2_56]
		snake_case__						:     Union[str, Any]		=in_proj_weight[2_56:5_12, :]
		snake_case__						:     List[str]		=in_proj_bias[2_56:5_12]
		snake_case__						:     Optional[int]		=in_proj_weight[-2_56:, :]
		snake_case__						:     List[str]		=in_proj_bias[-2_56:]







def 	lowercase_		(     ):






	"""simple docstring"""
	snake_case__						:     Union[str, Any]		='''http://images.cocodataset.org/val2017/000000039769.jpg'''
	snake_case__						:     Union[str, Any]		=Image.open(requests.get(UpperCamelCase_						,						stream=UpperCamelCase_ ).raw )

	return im







@torch.no_grad()
def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Any						,						SCREAMING_SNAKE_CASE						:		Tuple ):






	"""simple docstring"""
	snake_case__						:     List[Any]		=ConditionalDetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		snake_case__						:     Tuple		='''resnet101'''
	if "dc5" in model_name:
		snake_case__						:     int		=True
	snake_case__						:     int		='''panoptic''' in model_name
	if is_panoptic:
		snake_case__						:     Dict		=2_50
	else:
		snake_case__						:     Union[str, Any]		=91
		snake_case__						:     Any		='''huggingface/label-files'''
		snake_case__						:     Dict		='''coco-detection-id2label.json'''
		snake_case__						:     List[str]		=json.load(open(hf_hub_download(UpperCamelCase_						,						UpperCamelCase_						,						repo_type='''dataset''' )						,						'''r''' ) )
		snake_case__						:     Tuple		={int(UpperCamelCase_ ): v for k, v in idalabel.items()}
		snake_case__						:     int		=idalabel
		snake_case__						:     Any		={v: k for k, v in idalabel.items()}

	# load image processor
	snake_case__						:     str		='''coco_panoptic''' if is_panoptic else '''coco_detection'''
	snake_case__						:     List[Any]		=ConditionalDetrImageProcessor(format=UpperCamelCase_ )

	# prepare image
	snake_case__						:     Any		=prepare_img()
	snake_case__						:     Any		=image_processor(images=UpperCamelCase_						,						return_tensors='''pt''' )
	snake_case__						:     int		=encoding['''pixel_values''']

	logger.info(F'''Converting model {model_name}...''' )

	# load original model from torch hub
	snake_case__						:     Dict		=torch.hub.load('''DeppMeng/ConditionalDETR'''						,						UpperCamelCase_						,						pretrained=UpperCamelCase_ ).eval()
	snake_case__						:     Tuple		=conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			snake_case__						:     List[str]		='''conditional_detr.''' + src
		rename_key(UpperCamelCase_						,						UpperCamelCase_						,						UpperCamelCase_ )
	snake_case__						:     str		=rename_backbone_keys(UpperCamelCase_ )
	# query, key and value matrices need special treatment
	read_in_q_k_v(UpperCamelCase_						,						is_panoptic=UpperCamelCase_ )
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	snake_case__						:     Tuple		='''conditional_detr.model.''' if is_panoptic else '''model.'''
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('''conditional_detr''' )
			    and not key.startswith('''class_labels_classifier''' )
			    and not key.startswith('''bbox_predictor''' )
			):
				snake_case__						:     int		=state_dict.pop(UpperCamelCase_ )
				snake_case__						:     Optional[int]		=val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				snake_case__						:     List[str]		=state_dict.pop(UpperCamelCase_ )
				snake_case__						:     Any		=val
			elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
				continue
			else:
				snake_case__						:     Tuple		=state_dict.pop(UpperCamelCase_ )
				snake_case__						:     Optional[int]		=val
		else:
			if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
				snake_case__						:     Tuple		=state_dict.pop(UpperCamelCase_ )
				snake_case__						:     Any		=val
				# finally, create HuggingFace model and load state dict
	snake_case__						:     Tuple		=ConditionalDetrForSegmentation(UpperCamelCase_ ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase_ )
	model.load_state_dict(UpperCamelCase_ )
	model.eval()
	model.push_to_hub(repo_id=UpperCamelCase_						,						organization='''DepuMeng'''						,						commit_message='''Add model''' )
	# verify our conversion
	snake_case__						:     Tuple		=conditional_detr(UpperCamelCase_ )
	snake_case__						:     Optional[Any]		=model(UpperCamelCase_ )
	assert torch.allclose(outputs.logits						,						original_outputs['''pred_logits''']						,						atol=1E-4 )
	assert torch.allclose(outputs.pred_boxes						,						original_outputs['''pred_boxes''']						,						atol=1E-4 )
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks						,						original_outputs['''pred_masks''']						,						atol=1E-4 )

	# Save model and image processor
	logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
	Path(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
	model.save_pretrained(UpperCamelCase_ )
	image_processor.save_pretrained(UpperCamelCase_ )


if __name__ == "__main__":
		lowerCamelCase__            = argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    default='''conditional_detr_resnet50''',
		    type=str,
		    help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		lowerCamelCase__            = parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


_SCREAMING_SNAKE_CASE 		=      ["""text""", """image""", """audio"""]





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for input_type in input_types:
  if input_type == "text":
   inputs.append("""Text input"""       )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       ).resize((512, 512)       )       )
  elif input_type == "audio":
   inputs.append(torch.ones(3000       )       )
  elif isinstance(UpperCamelCase_							,					UpperCamelCase_       ):
   inputs.append(create_inputs(UpperCamelCase_       )       )
  else:
   raise ValueError(f"""Invalid type requested: {input_type}"""       )

 return inputs





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for output in outputs:
  if isinstance(UpperCamelCase_							,					(str, AgentText)       ):
   output_types.append("""text"""       )
  elif isinstance(UpperCamelCase_							,					(Image.Image, AgentImage)       ):
   output_types.append("""image"""       )
  elif isinstance(UpperCamelCase_							,					(torch.Tensor, AgentAudio)       ):
   output_types.append("""audio"""       )
  else:
   raise ValueError(f"""Invalid output: {output}"""       )

 return output_types


@is_tool_test
class SCREAMING_SNAKE_CASE_		:







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """inputs"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """outputs"""				)				)

    UpperCamelCase			  =      self.tool.inputs
    for _input in inputs:
     if isinstance(_input   ,   lowerCamelCase_				):
      for __input in _input:
       self.assertTrue(__input in authorized_types				)
     else:
      self.assertTrue(_input in authorized_types				)

    UpperCamelCase			  =      self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    # There is a single output
    if len(self.tool.outputs				) == 1:
     UpperCamelCase			  =      [outputs]

    self.assertListEqual(output_types(lowerCamelCase_				)   ,   self.tool.outputs				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """description"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """default_checkpoint"""				)				)
    self.assertTrue(self.tool.description.startswith("""This is a tool that"""				)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)

    for output, output_type in zip(lowerCamelCase_   ,   self.tool.outputs				):
     UpperCamelCase			  =      AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)

    UpperCamelCase			  =      []

    for _input, input_type in zip(lowerCamelCase_   ,   self.tool.inputs				):
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)
